{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T14:17:34Z","timestamp":1770992254277,"version":"3.50.1"},"reference-count":31,"publisher":"EDP Sciences","issue":"2","license":[{"start":{"date-parts":[[2019,4,18]],"date-time":"2019-04-18T00:00:00Z","timestamp":1555545600000},"content-version":"vor","delay-in-days":17,"URL":"https:\/\/www.edpsciences.org\/en\/authors\/copyright-and-licensing"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["RAIRO-Oper. Res."],"accepted":{"date-parts":[[2017,6,10]]},"published-print":{"date-parts":[[2019,4]]},"abstract":"<jats:p>The goal of QoS aware web service composition (QoS-WSC) is to provide new functionalities and find a best combination of services to meet complex needs of users. QoS of the resulting composite service should be optimized. QoS-WSC is a global multi-objective optimization problem belonging to NP-hard class given the number of available services. Most of existing approaches reduce this problem to a single-objective problem by aggregating different objectives, which leads to a loss of information. An alternative issue is to use Pareto-based approaches. The Pareto-optimal set contains solutions that ensure the best trade-off between conflicting objectives. In this paper, a new multi-objective meta-heuristic bio-inspired Pareto-based approach is presented to address the QoS-WSC, it is based on Elephants Herding Optimization (EHO) algorithm. EHO is characterised by a strategy of dividing and combining the population to sub population (clan) which allows exchange of information between local searches to get a global optimum. However, the application of others evolutionary algorithms to this problem cannot avoids the early stagnancy in a local optimum. In this paper a discrete and multi-objective version of EHO will be presented based on a crossover operator. Compared with SPEA2 (Strength Pareto Evolutionary Algorithm 2) and MOPSO (Multi-Objective Particle Swarm Optimization algorithm), the results of experimental evaluation show that our improvements significantly outperform the existing algorithms in term of Hypervolume, Set Coverage and Spacing metrics.<\/jats:p>","DOI":"10.1051\/ro\/2017049","type":"journal-article","created":{"date-parts":[[2017,6,13]],"date-time":"2017-06-13T06:37:10Z","timestamp":1497335830000},"page":"445-459","source":"Crossref","is-referenced-by-count":18,"title":["Multi-objective and discrete Elephants Herding Optimization algorithm for QoS aware web service composition"],"prefix":"10.1051","volume":"53","author":[{"given":"Samia","family":"Chibani Sadouki","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdelkamel","family":"Tari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"250","published-online":{"date-parts":[[2019,4,18]]},"reference":[{"key":"R1","doi-asserted-by":"crossref","unstructured":"Al-Masri E. and Mahmoud Q.H., QoS-based discovery and ranking of web services. 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